RNA-seq Summary

Haiwang Yang

2017-11-11

This is a summary of RNA-seq differential expression using the DESeq2 package. This document will start with a set of QC metrics. I then proceed with several different differential expression models.

Experimental Design

The below table shows the basic experimental design for the RNA-seq.

Pairwise Correlations

All pairwise correlations of gene expression among samples

R output

## NULL

Heatmap of Euclidean distance among samples

PCA to group samples by the two largest principal components.

Differential Expression

Sex Effects

Model: counts ~ sex

## [1] "Intercept" "sexmale"   "sexfemale"

## 
## out of 13900 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)     : 6143, 44% 
## LFC < 0 (down)   : 4522, 33% 
## outliers [1]     : 0, 0% 
## low counts [2]   : 0, 0% 
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results